docs: minor updates for js migration guides (#1451)

Co-authored-by: Will Jones <willjones127@gmail.com>

docs/src/js/namespaces/embedding/classes/EmbeddingFunction.md

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+[**@lancedb/lancedb**](../../../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../../../globals.md) / [embedding](../README.md) / EmbeddingFunction
+
+# Class: `abstract` EmbeddingFunction<T, M>
+
+An embedding function that automatically creates vector representation for a given column.
+
+## Extended by
+
+- [`OpenAIEmbeddingFunction`](OpenAIEmbeddingFunction.md)
+
+## Type Parameters
+
+• **T** = `any`
+
+• **M** *extends* `FunctionOptions` = `FunctionOptions`
+
+## Constructors
+
+### new EmbeddingFunction()
+
+> **new EmbeddingFunction**<`T`, `M`>(): [`EmbeddingFunction`](EmbeddingFunction.md)<`T`, `M`>
+
+#### Returns
+
+[`EmbeddingFunction`](EmbeddingFunction.md)<`T`, `M`>
+
+## Methods
+
+### computeQueryEmbeddings()
+
+> **computeQueryEmbeddings**(`data`): `Promise`<`number`[] \| `Float32Array` \| `Float64Array`>
+
+Compute the embeddings for a single query
+
+#### Parameters
+
+• **data**: `T`
+
+#### Returns
+
+`Promise`<`number`[] \| `Float32Array` \| `Float64Array`>
+
+***
+
+### computeSourceEmbeddings()
+
+> `abstract` **computeSourceEmbeddings**(`data`): `Promise`<`number`[][] \| `Float32Array`[] \| `Float64Array`[]>
+
+Creates a vector representation for the given values.
+
+#### Parameters
+
+• **data**: `T`[]
+
+#### Returns
+
+`Promise`<`number`[][] \| `Float32Array`[] \| `Float64Array`[]>
+
+***
+
+### embeddingDataType()
+
+> `abstract` **embeddingDataType**(): `Float`<`Floats`>
+
+The datatype of the embeddings
+
+#### Returns
+
+`Float`<`Floats`>
+
+***
+
+### ndims()
+
+> **ndims**(): `undefined` \| `number`
+
+The number of dimensions of the embeddings
+
+#### Returns
+
+`undefined` \| `number`
+
+***
+
+### sourceField()
+
+> **sourceField**(`optionsOrDatatype`): [`DataType`<`Type`, `any`>, `Map`<`string`, [`EmbeddingFunction`](EmbeddingFunction.md)<`any`, `FunctionOptions`>>]
+
+sourceField is used in combination with `LanceSchema` to provide a declarative data model
+
+#### Parameters
+
+• **optionsOrDatatype**: `DataType`<`Type`, `any`> \| `Partial`<`FieldOptions`<`DataType`<`Type`, `any`>>>
+
+The options for the field or the datatype
+
+#### Returns
+
+[`DataType`<`Type`, `any`>, `Map`<`string`, [`EmbeddingFunction`](EmbeddingFunction.md)<`any`, `FunctionOptions`>>]
+
+#### See
+
+lancedb.LanceSchema
+
+***
+
+### toJSON()
+
+> `abstract` **toJSON**(): `Partial`<`M`>
+
+Convert the embedding function to a JSON object
+It is used to serialize the embedding function to the schema
+It's important that any object returned by this method contains all the necessary
+information to recreate the embedding function
+
+It should return the same object that was passed to the constructor
+If it does not, the embedding function will not be able to be recreated, or could be recreated incorrectly
+
+#### Returns
+
+`Partial`<`M`>
+
+#### Example
+
+```ts
+class MyEmbeddingFunction extends EmbeddingFunction {
+  constructor(options: {model: string, timeout: number}) {
+    super();
+    this.model = options.model;
+    this.timeout = options.timeout;
+  }
+  toJSON() {
+    return {
+      model: this.model,
+      timeout: this.timeout,
+    };
+}
+```
+
+***
+
+### vectorField()
+
+> **vectorField**(`optionsOrDatatype`?): [`DataType`<`Type`, `any`>, `Map`<`string`, [`EmbeddingFunction`](EmbeddingFunction.md)<`any`, `FunctionOptions`>>]
+
+vectorField is used in combination with `LanceSchema` to provide a declarative data model
+
+#### Parameters
+
+• **optionsOrDatatype?**: `DataType`<`Type`, `any`> \| `Partial`<`FieldOptions`<`DataType`<`Type`, `any`>>>
+
+#### Returns
+
+[`DataType`<`Type`, `any`>, `Map`<`string`, [`EmbeddingFunction`](EmbeddingFunction.md)<`any`, `FunctionOptions`>>]
+
+#### See
+
+lancedb.LanceSchema